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  <h1>Source code for torch.nn.init</h1><div class="highlight"><pre>
<span></span><span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">division</span>

<span class="kn">import</span> <span class="nn">math</span>
<span class="kn">import</span> <span class="nn">warnings</span>

<span class="kn">import</span> <span class="nn">torch</span>

<span class="c1"># These no_grad_* functions are necessary as wrappers around the parts of these</span>
<span class="c1"># functions that use `with torch.no_grad()`. The JIT doesn&#39;t support context</span>
<span class="c1"># managers, so these need to be implemented as builtins. Using these wrappers</span>
<span class="c1"># lets us keep those builtins small and re-usable.</span>
<span class="k">def</span> <span class="nf">_no_grad_uniform_</span><span class="p">(</span><span class="n">tensor</span><span class="p">,</span> <span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">):</span>
    <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
        <span class="k">return</span> <span class="n">tensor</span><span class="o">.</span><span class="n">uniform_</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">)</span>


<span class="k">def</span> <span class="nf">_no_grad_normal_</span><span class="p">(</span><span class="n">tensor</span><span class="p">,</span> <span class="n">mean</span><span class="p">,</span> <span class="n">std</span><span class="p">):</span>
    <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
        <span class="k">return</span> <span class="n">tensor</span><span class="o">.</span><span class="n">normal_</span><span class="p">(</span><span class="n">mean</span><span class="p">,</span> <span class="n">std</span><span class="p">)</span>


<span class="k">def</span> <span class="nf">_no_grad_fill_</span><span class="p">(</span><span class="n">tensor</span><span class="p">,</span> <span class="n">val</span><span class="p">):</span>
    <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
        <span class="k">return</span> <span class="n">tensor</span><span class="o">.</span><span class="n">fill_</span><span class="p">(</span><span class="n">val</span><span class="p">)</span>


<span class="k">def</span> <span class="nf">_no_grad_zero_</span><span class="p">(</span><span class="n">tensor</span><span class="p">):</span>
    <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
        <span class="k">return</span> <span class="n">tensor</span><span class="o">.</span><span class="n">zero_</span><span class="p">()</span>


<div class="viewcode-block" id="calculate_gain"><a class="viewcode-back" href="../../../nn.init.html#torch.nn.init.calculate_gain">[docs]</a><span class="k">def</span> <span class="nf">calculate_gain</span><span class="p">(</span><span class="n">nonlinearity</span><span class="p">,</span> <span class="n">param</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Return the recommended gain value for the given nonlinearity function.</span>
<span class="sd">    The values are as follows:</span>

<span class="sd">    ================= ====================================================</span>
<span class="sd">    nonlinearity      gain</span>
<span class="sd">    ================= ====================================================</span>
<span class="sd">    Linear / Identity :math:`1`</span>
<span class="sd">    Conv{1,2,3}D      :math:`1`</span>
<span class="sd">    Sigmoid           :math:`1`</span>
<span class="sd">    Tanh              :math:`\frac{5}{3}`</span>
<span class="sd">    ReLU              :math:`\sqrt{2}`</span>
<span class="sd">    Leaky Relu        :math:`\sqrt{\frac{2}{1 + \text{negative\_slope}^2}}`</span>
<span class="sd">    ================= ====================================================</span>

<span class="sd">    Args:</span>
<span class="sd">        nonlinearity: the non-linear function (`nn.functional` name)</span>
<span class="sd">        param: optional parameter for the non-linear function</span>

<span class="sd">    Examples:</span>
<span class="sd">        &gt;&gt;&gt; gain = nn.init.calculate_gain(&#39;leaky_relu&#39;, 0.2)  # leaky_relu with negative_slope=0.2</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="n">linear_fns</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;linear&#39;</span><span class="p">,</span> <span class="s1">&#39;conv1d&#39;</span><span class="p">,</span> <span class="s1">&#39;conv2d&#39;</span><span class="p">,</span> <span class="s1">&#39;conv3d&#39;</span><span class="p">,</span> <span class="s1">&#39;conv_transpose1d&#39;</span><span class="p">,</span> <span class="s1">&#39;conv_transpose2d&#39;</span><span class="p">,</span> <span class="s1">&#39;conv_transpose3d&#39;</span><span class="p">]</span>
    <span class="k">if</span> <span class="n">nonlinearity</span> <span class="ow">in</span> <span class="n">linear_fns</span> <span class="ow">or</span> <span class="n">nonlinearity</span> <span class="o">==</span> <span class="s1">&#39;sigmoid&#39;</span><span class="p">:</span>
        <span class="k">return</span> <span class="mi">1</span>
    <span class="k">elif</span> <span class="n">nonlinearity</span> <span class="o">==</span> <span class="s1">&#39;tanh&#39;</span><span class="p">:</span>
        <span class="k">return</span> <span class="mf">5.0</span> <span class="o">/</span> <span class="mi">3</span>
    <span class="k">elif</span> <span class="n">nonlinearity</span> <span class="o">==</span> <span class="s1">&#39;relu&#39;</span><span class="p">:</span>
        <span class="k">return</span> <span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mf">2.0</span><span class="p">)</span>
    <span class="k">elif</span> <span class="n">nonlinearity</span> <span class="o">==</span> <span class="s1">&#39;leaky_relu&#39;</span><span class="p">:</span>
        <span class="k">if</span> <span class="n">param</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
            <span class="n">negative_slope</span> <span class="o">=</span> <span class="mf">0.01</span>
        <span class="k">elif</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">param</span><span class="p">,</span> <span class="nb">bool</span><span class="p">)</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">param</span><span class="p">,</span> <span class="nb">int</span><span class="p">)</span> <span class="ow">or</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">param</span><span class="p">,</span> <span class="nb">float</span><span class="p">):</span>
            <span class="c1"># True/False are instances of int, hence check above</span>
            <span class="n">negative_slope</span> <span class="o">=</span> <span class="n">param</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;negative_slope </span><span class="si">{}</span><span class="s2"> not a valid number&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">param</span><span class="p">))</span>
        <span class="k">return</span> <span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mf">2.0</span> <span class="o">/</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">negative_slope</span> <span class="o">**</span> <span class="mi">2</span><span class="p">))</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Unsupported nonlinearity </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">nonlinearity</span><span class="p">))</span></div>


<div class="viewcode-block" id="uniform_"><a class="viewcode-back" href="../../../nn.init.html#torch.nn.init.uniform_">[docs]</a><span class="k">def</span> <span class="nf">uniform_</span><span class="p">(</span><span class="n">tensor</span><span class="p">,</span> <span class="n">a</span><span class="o">=</span><span class="mf">0.</span><span class="p">,</span> <span class="n">b</span><span class="o">=</span><span class="mf">1.</span><span class="p">):</span>
    <span class="c1"># type: (Tensor, float, float) -&gt; Tensor</span>
    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Fills the input Tensor with values drawn from the uniform</span>
<span class="sd">    distribution :math:`\mathcal{U}(a, b)`.</span>

<span class="sd">    Args:</span>
<span class="sd">        tensor: an n-dimensional `torch.Tensor`</span>
<span class="sd">        a: the lower bound of the uniform distribution</span>
<span class="sd">        b: the upper bound of the uniform distribution</span>

<span class="sd">    Examples:</span>
<span class="sd">        &gt;&gt;&gt; w = torch.empty(3, 5)</span>
<span class="sd">        &gt;&gt;&gt; nn.init.uniform_(w)</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">return</span> <span class="n">_no_grad_uniform_</span><span class="p">(</span><span class="n">tensor</span><span class="p">,</span> <span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">)</span></div>


<div class="viewcode-block" id="normal_"><a class="viewcode-back" href="../../../nn.init.html#torch.nn.init.normal_">[docs]</a><span class="k">def</span> <span class="nf">normal_</span><span class="p">(</span><span class="n">tensor</span><span class="p">,</span> <span class="n">mean</span><span class="o">=</span><span class="mf">0.</span><span class="p">,</span> <span class="n">std</span><span class="o">=</span><span class="mf">1.</span><span class="p">):</span>
    <span class="c1"># type: (Tensor, float, float) -&gt; Tensor</span>
    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Fills the input Tensor with values drawn from the normal</span>
<span class="sd">    distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)`.</span>

<span class="sd">    Args:</span>
<span class="sd">        tensor: an n-dimensional `torch.Tensor`</span>
<span class="sd">        mean: the mean of the normal distribution</span>
<span class="sd">        std: the standard deviation of the normal distribution</span>

<span class="sd">    Examples:</span>
<span class="sd">        &gt;&gt;&gt; w = torch.empty(3, 5)</span>
<span class="sd">        &gt;&gt;&gt; nn.init.normal_(w)</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">return</span> <span class="n">_no_grad_normal_</span><span class="p">(</span><span class="n">tensor</span><span class="p">,</span> <span class="n">mean</span><span class="p">,</span> <span class="n">std</span><span class="p">)</span></div>


<div class="viewcode-block" id="constant_"><a class="viewcode-back" href="../../../nn.init.html#torch.nn.init.constant_">[docs]</a><span class="k">def</span> <span class="nf">constant_</span><span class="p">(</span><span class="n">tensor</span><span class="p">,</span> <span class="n">val</span><span class="p">):</span>
    <span class="c1"># type: (Tensor, float) -&gt; Tensor</span>
    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Fills the input Tensor with the value :math:`\text{val}`.</span>

<span class="sd">    Args:</span>
<span class="sd">        tensor: an n-dimensional `torch.Tensor`</span>
<span class="sd">        val: the value to fill the tensor with</span>

<span class="sd">    Examples:</span>
<span class="sd">        &gt;&gt;&gt; w = torch.empty(3, 5)</span>
<span class="sd">        &gt;&gt;&gt; nn.init.constant_(w, 0.3)</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">return</span> <span class="n">_no_grad_fill_</span><span class="p">(</span><span class="n">tensor</span><span class="p">,</span> <span class="n">val</span><span class="p">)</span></div>


<div class="viewcode-block" id="ones_"><a class="viewcode-back" href="../../../nn.init.html#torch.nn.init.ones_">[docs]</a><span class="k">def</span> <span class="nf">ones_</span><span class="p">(</span><span class="n">tensor</span><span class="p">):</span>
    <span class="c1"># type: (Tensor) -&gt; Tensor</span>
    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Fills the input Tensor with the scalar value `1`.</span>

<span class="sd">    Args:</span>
<span class="sd">        tensor: an n-dimensional `torch.Tensor`</span>

<span class="sd">    Examples:</span>
<span class="sd">        &gt;&gt;&gt; w = torch.empty(3, 5)</span>
<span class="sd">        &gt;&gt;&gt; nn.init.ones_(w)</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">return</span> <span class="n">_no_grad_fill_</span><span class="p">(</span><span class="n">tensor</span><span class="p">,</span> <span class="mf">1.</span><span class="p">)</span></div>


<div class="viewcode-block" id="zeros_"><a class="viewcode-back" href="../../../nn.init.html#torch.nn.init.zeros_">[docs]</a><span class="k">def</span> <span class="nf">zeros_</span><span class="p">(</span><span class="n">tensor</span><span class="p">):</span>
    <span class="c1"># type: (Tensor) -&gt; Tensor</span>
    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Fills the input Tensor with the scalar value `0`.</span>

<span class="sd">    Args:</span>
<span class="sd">        tensor: an n-dimensional `torch.Tensor`</span>

<span class="sd">    Examples:</span>
<span class="sd">        &gt;&gt;&gt; w = torch.empty(3, 5)</span>
<span class="sd">        &gt;&gt;&gt; nn.init.zeros_(w)</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">return</span> <span class="n">_no_grad_zero_</span><span class="p">(</span><span class="n">tensor</span><span class="p">)</span></div>


<div class="viewcode-block" id="eye_"><a class="viewcode-back" href="../../../nn.init.html#torch.nn.init.eye_">[docs]</a><span class="k">def</span> <span class="nf">eye_</span><span class="p">(</span><span class="n">tensor</span><span class="p">):</span>
    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Fills the 2-dimensional input `Tensor` with the identity</span>
<span class="sd">    matrix. Preserves the identity of the inputs in `Linear` layers, where as</span>
<span class="sd">    many inputs are preserved as possible.</span>

<span class="sd">    Args:</span>
<span class="sd">        tensor: a 2-dimensional `torch.Tensor`</span>

<span class="sd">    Examples:</span>
<span class="sd">        &gt;&gt;&gt; w = torch.empty(3, 5)</span>
<span class="sd">        &gt;&gt;&gt; nn.init.eye_(w)</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">if</span> <span class="n">tensor</span><span class="o">.</span><span class="n">ndimension</span><span class="p">()</span> <span class="o">!=</span> <span class="mi">2</span><span class="p">:</span>
        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Only tensors with 2 dimensions are supported&quot;</span><span class="p">)</span>

    <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
        <span class="n">torch</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="o">*</span><span class="n">tensor</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">out</span><span class="o">=</span><span class="n">tensor</span><span class="p">,</span> <span class="n">requires_grad</span><span class="o">=</span><span class="n">tensor</span><span class="o">.</span><span class="n">requires_grad</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">tensor</span></div>


<div class="viewcode-block" id="dirac_"><a class="viewcode-back" href="../../../nn.init.html#torch.nn.init.dirac_">[docs]</a><span class="k">def</span> <span class="nf">dirac_</span><span class="p">(</span><span class="n">tensor</span><span class="p">,</span> <span class="n">groups</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Fills the {3, 4, 5}-dimensional input `Tensor` with the Dirac</span>
<span class="sd">    delta function. Preserves the identity of the inputs in `Convolutional`</span>
<span class="sd">    layers, where as many input channels are preserved as possible. In case</span>
<span class="sd">    of groups&gt;1, each group of channels preserves identity</span>

<span class="sd">    Args:</span>
<span class="sd">        tensor: a {3, 4, 5}-dimensional `torch.Tensor`</span>
<span class="sd">        groups (optional): number of groups in the conv layer (default: 1)</span>
<span class="sd">    Examples:</span>
<span class="sd">        &gt;&gt;&gt; w = torch.empty(3, 16, 5, 5)</span>
<span class="sd">        &gt;&gt;&gt; nn.init.dirac_(w)</span>
<span class="sd">        &gt;&gt;&gt; w = torch.empty(3, 24, 5, 5)</span>
<span class="sd">        &gt;&gt;&gt; nn.init.dirac_(w, 3)</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="n">dimensions</span> <span class="o">=</span> <span class="n">tensor</span><span class="o">.</span><span class="n">ndimension</span><span class="p">()</span>
    <span class="k">if</span> <span class="n">dimensions</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">]:</span>
        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Only tensors with 3, 4, or 5 dimensions are supported&quot;</span><span class="p">)</span>

    <span class="n">sizes</span> <span class="o">=</span> <span class="n">tensor</span><span class="o">.</span><span class="n">size</span><span class="p">()</span>

    <span class="k">if</span> <span class="n">sizes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">%</span> <span class="n">groups</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;dim 0 must be divisible by groups&#39;</span><span class="p">)</span>

    <span class="n">out_chans_per_grp</span> <span class="o">=</span> <span class="n">sizes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">//</span> <span class="n">groups</span>
    <span class="n">min_dim</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">out_chans_per_grp</span><span class="p">,</span> <span class="n">sizes</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>

    <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
        <span class="n">tensor</span><span class="o">.</span><span class="n">zero_</span><span class="p">()</span>

        <span class="k">for</span> <span class="n">g</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">groups</span><span class="p">):</span>
            <span class="k">for</span> <span class="n">d</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">min_dim</span><span class="p">):</span>
                <span class="k">if</span> <span class="n">dimensions</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>  <span class="c1"># Temporal convolution</span>
                    <span class="n">tensor</span><span class="p">[</span><span class="n">g</span> <span class="o">*</span> <span class="n">out_chans_per_grp</span> <span class="o">+</span> <span class="n">d</span><span class="p">,</span> <span class="n">d</span><span class="p">,</span> <span class="n">tensor</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span> <span class="o">//</span> <span class="mi">2</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
                <span class="k">elif</span> <span class="n">dimensions</span> <span class="o">==</span> <span class="mi">4</span><span class="p">:</span>  <span class="c1"># Spatial convolution</span>
                    <span class="n">tensor</span><span class="p">[</span><span class="n">g</span> <span class="o">*</span> <span class="n">out_chans_per_grp</span> <span class="o">+</span> <span class="n">d</span><span class="p">,</span> <span class="n">d</span><span class="p">,</span> <span class="n">tensor</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span> <span class="o">//</span> <span class="mi">2</span><span class="p">,</span>
                           <span class="n">tensor</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span> <span class="o">//</span> <span class="mi">2</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
                <span class="k">else</span><span class="p">:</span>  <span class="c1"># Volumetric convolution</span>
                    <span class="n">tensor</span><span class="p">[</span><span class="n">g</span> <span class="o">*</span> <span class="n">out_chans_per_grp</span> <span class="o">+</span> <span class="n">d</span><span class="p">,</span> <span class="n">d</span><span class="p">,</span> <span class="n">tensor</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span> <span class="o">//</span> <span class="mi">2</span><span class="p">,</span>
                           <span class="n">tensor</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span> <span class="o">//</span> <span class="mi">2</span><span class="p">,</span> <span class="n">tensor</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">4</span><span class="p">)</span> <span class="o">//</span> <span class="mi">2</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
    <span class="k">return</span> <span class="n">tensor</span></div>


<span class="k">def</span> <span class="nf">_calculate_fan_in_and_fan_out</span><span class="p">(</span><span class="n">tensor</span><span class="p">):</span>
    <span class="n">dimensions</span> <span class="o">=</span> <span class="n">tensor</span><span class="o">.</span><span class="n">dim</span><span class="p">()</span>
    <span class="k">if</span> <span class="n">dimensions</span> <span class="o">&lt;</span> <span class="mi">2</span><span class="p">:</span>
        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Fan in and fan out can not be computed for tensor with fewer than 2 dimensions&quot;</span><span class="p">)</span>

    <span class="n">num_input_fmaps</span> <span class="o">=</span> <span class="n">tensor</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
    <span class="n">num_output_fmaps</span> <span class="o">=</span> <span class="n">tensor</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
    <span class="n">receptive_field_size</span> <span class="o">=</span> <span class="mi">1</span>
    <span class="k">if</span> <span class="n">tensor</span><span class="o">.</span><span class="n">dim</span><span class="p">()</span> <span class="o">&gt;</span> <span class="mi">2</span><span class="p">:</span>
        <span class="n">receptive_field_size</span> <span class="o">=</span> <span class="n">tensor</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">numel</span><span class="p">()</span>
    <span class="n">fan_in</span> <span class="o">=</span> <span class="n">num_input_fmaps</span> <span class="o">*</span> <span class="n">receptive_field_size</span>
    <span class="n">fan_out</span> <span class="o">=</span> <span class="n">num_output_fmaps</span> <span class="o">*</span> <span class="n">receptive_field_size</span>

    <span class="k">return</span> <span class="n">fan_in</span><span class="p">,</span> <span class="n">fan_out</span>


<div class="viewcode-block" id="xavier_uniform_"><a class="viewcode-back" href="../../../nn.init.html#torch.nn.init.xavier_uniform_">[docs]</a><span class="k">def</span> <span class="nf">xavier_uniform_</span><span class="p">(</span><span class="n">tensor</span><span class="p">,</span> <span class="n">gain</span><span class="o">=</span><span class="mf">1.</span><span class="p">):</span>
    <span class="c1"># type: (Tensor, float) -&gt; Tensor</span>
    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Fills the input `Tensor` with values according to the method</span>
<span class="sd">    described in `Understanding the difficulty of training deep feedforward</span>
<span class="sd">    neural networks` - Glorot, X. &amp; Bengio, Y. (2010), using a uniform</span>
<span class="sd">    distribution. The resulting tensor will have values sampled from</span>
<span class="sd">    :math:`\mathcal{U}(-a, a)` where</span>

<span class="sd">    .. math::</span>
<span class="sd">        a = \text{gain} \times \sqrt{\frac{6}{\text{fan\_in} + \text{fan\_out}}}</span>

<span class="sd">    Also known as Glorot initialization.</span>

<span class="sd">    Args:</span>
<span class="sd">        tensor: an n-dimensional `torch.Tensor`</span>
<span class="sd">        gain: an optional scaling factor</span>

<span class="sd">    Examples:</span>
<span class="sd">        &gt;&gt;&gt; w = torch.empty(3, 5)</span>
<span class="sd">        &gt;&gt;&gt; nn.init.xavier_uniform_(w, gain=nn.init.calculate_gain(&#39;relu&#39;))</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="n">fan_in</span><span class="p">,</span> <span class="n">fan_out</span> <span class="o">=</span> <span class="n">_calculate_fan_in_and_fan_out</span><span class="p">(</span><span class="n">tensor</span><span class="p">)</span>
    <span class="n">std</span> <span class="o">=</span> <span class="n">gain</span> <span class="o">*</span> <span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mf">2.0</span> <span class="o">/</span> <span class="nb">float</span><span class="p">(</span><span class="n">fan_in</span> <span class="o">+</span> <span class="n">fan_out</span><span class="p">))</span>
    <span class="n">a</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mf">3.0</span><span class="p">)</span> <span class="o">*</span> <span class="n">std</span>  <span class="c1"># Calculate uniform bounds from standard deviation</span>

    <span class="k">return</span> <span class="n">_no_grad_uniform_</span><span class="p">(</span><span class="n">tensor</span><span class="p">,</span> <span class="o">-</span><span class="n">a</span><span class="p">,</span> <span class="n">a</span><span class="p">)</span></div>


<div class="viewcode-block" id="xavier_normal_"><a class="viewcode-back" href="../../../nn.init.html#torch.nn.init.xavier_normal_">[docs]</a><span class="k">def</span> <span class="nf">xavier_normal_</span><span class="p">(</span><span class="n">tensor</span><span class="p">,</span> <span class="n">gain</span><span class="o">=</span><span class="mf">1.</span><span class="p">):</span>
    <span class="c1"># type: (Tensor, float) -&gt; Tensor</span>
    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Fills the input `Tensor` with values according to the method</span>
<span class="sd">    described in `Understanding the difficulty of training deep feedforward</span>
<span class="sd">    neural networks` - Glorot, X. &amp; Bengio, Y. (2010), using a normal</span>
<span class="sd">    distribution. The resulting tensor will have values sampled from</span>
<span class="sd">    :math:`\mathcal{N}(0, \text{std}^2)` where</span>

<span class="sd">    .. math::</span>
<span class="sd">        \text{std} = \text{gain} \times \sqrt{\frac{2}{\text{fan\_in} + \text{fan\_out}}}</span>

<span class="sd">    Also known as Glorot initialization.</span>

<span class="sd">    Args:</span>
<span class="sd">        tensor: an n-dimensional `torch.Tensor`</span>
<span class="sd">        gain: an optional scaling factor</span>

<span class="sd">    Examples:</span>
<span class="sd">        &gt;&gt;&gt; w = torch.empty(3, 5)</span>
<span class="sd">        &gt;&gt;&gt; nn.init.xavier_normal_(w)</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="n">fan_in</span><span class="p">,</span> <span class="n">fan_out</span> <span class="o">=</span> <span class="n">_calculate_fan_in_and_fan_out</span><span class="p">(</span><span class="n">tensor</span><span class="p">)</span>
    <span class="n">std</span> <span class="o">=</span> <span class="n">gain</span> <span class="o">*</span> <span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mf">2.0</span> <span class="o">/</span> <span class="nb">float</span><span class="p">(</span><span class="n">fan_in</span> <span class="o">+</span> <span class="n">fan_out</span><span class="p">))</span>

    <span class="k">return</span> <span class="n">_no_grad_normal_</span><span class="p">(</span><span class="n">tensor</span><span class="p">,</span> <span class="mf">0.</span><span class="p">,</span> <span class="n">std</span><span class="p">)</span></div>


<span class="k">def</span> <span class="nf">_calculate_correct_fan</span><span class="p">(</span><span class="n">tensor</span><span class="p">,</span> <span class="n">mode</span><span class="p">):</span>
    <span class="n">mode</span> <span class="o">=</span> <span class="n">mode</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span>
    <span class="n">valid_modes</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;fan_in&#39;</span><span class="p">,</span> <span class="s1">&#39;fan_out&#39;</span><span class="p">]</span>
    <span class="k">if</span> <span class="n">mode</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">valid_modes</span><span class="p">:</span>
        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Mode </span><span class="si">{}</span><span class="s2"> not supported, please use one of </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">mode</span><span class="p">,</span> <span class="n">valid_modes</span><span class="p">))</span>

    <span class="n">fan_in</span><span class="p">,</span> <span class="n">fan_out</span> <span class="o">=</span> <span class="n">_calculate_fan_in_and_fan_out</span><span class="p">(</span><span class="n">tensor</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">fan_in</span> <span class="k">if</span> <span class="n">mode</span> <span class="o">==</span> <span class="s1">&#39;fan_in&#39;</span> <span class="k">else</span> <span class="n">fan_out</span>


<div class="viewcode-block" id="kaiming_uniform_"><a class="viewcode-back" href="../../../nn.init.html#torch.nn.init.kaiming_uniform_">[docs]</a><span class="k">def</span> <span class="nf">kaiming_uniform_</span><span class="p">(</span><span class="n">tensor</span><span class="p">,</span> <span class="n">a</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;fan_in&#39;</span><span class="p">,</span> <span class="n">nonlinearity</span><span class="o">=</span><span class="s1">&#39;leaky_relu&#39;</span><span class="p">):</span>
    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Fills the input `Tensor` with values according to the method</span>
<span class="sd">    described in `Delving deep into rectifiers: Surpassing human-level</span>
<span class="sd">    performance on ImageNet classification` - He, K. et al. (2015), using a</span>
<span class="sd">    uniform distribution. The resulting tensor will have values sampled from</span>
<span class="sd">    :math:`\mathcal{U}(-\text{bound}, \text{bound})` where</span>

<span class="sd">    .. math::</span>
<span class="sd">        \text{bound} = \text{gain} \times \sqrt{\frac{3}{\text{fan\_mode}}}</span>

<span class="sd">    Also known as He initialization.</span>

<span class="sd">    Args:</span>
<span class="sd">        tensor: an n-dimensional `torch.Tensor`</span>
<span class="sd">        a: the negative slope of the rectifier used after this layer (only </span>
<span class="sd">        used with ``&#39;leaky_relu&#39;``)</span>
<span class="sd">        mode: either ``&#39;fan_in&#39;`` (default) or ``&#39;fan_out&#39;``. Choosing ``&#39;fan_in&#39;``</span>
<span class="sd">            preserves the magnitude of the variance of the weights in the</span>
<span class="sd">            forward pass. Choosing ``&#39;fan_out&#39;`` preserves the magnitudes in the</span>
<span class="sd">            backwards pass.</span>
<span class="sd">        nonlinearity: the non-linear function (`nn.functional` name),</span>
<span class="sd">            recommended to use only with ``&#39;relu&#39;`` or ``&#39;leaky_relu&#39;`` (default).</span>

<span class="sd">    Examples:</span>
<span class="sd">        &gt;&gt;&gt; w = torch.empty(3, 5)</span>
<span class="sd">        &gt;&gt;&gt; nn.init.kaiming_uniform_(w, mode=&#39;fan_in&#39;, nonlinearity=&#39;relu&#39;)</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="n">fan</span> <span class="o">=</span> <span class="n">_calculate_correct_fan</span><span class="p">(</span><span class="n">tensor</span><span class="p">,</span> <span class="n">mode</span><span class="p">)</span>
    <span class="n">gain</span> <span class="o">=</span> <span class="n">calculate_gain</span><span class="p">(</span><span class="n">nonlinearity</span><span class="p">,</span> <span class="n">a</span><span class="p">)</span>
    <span class="n">std</span> <span class="o">=</span> <span class="n">gain</span> <span class="o">/</span> <span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">fan</span><span class="p">)</span>
    <span class="n">bound</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mf">3.0</span><span class="p">)</span> <span class="o">*</span> <span class="n">std</span>  <span class="c1"># Calculate uniform bounds from standard deviation</span>
    <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
        <span class="k">return</span> <span class="n">tensor</span><span class="o">.</span><span class="n">uniform_</span><span class="p">(</span><span class="o">-</span><span class="n">bound</span><span class="p">,</span> <span class="n">bound</span><span class="p">)</span></div>


<div class="viewcode-block" id="kaiming_normal_"><a class="viewcode-back" href="../../../nn.init.html#torch.nn.init.kaiming_normal_">[docs]</a><span class="k">def</span> <span class="nf">kaiming_normal_</span><span class="p">(</span><span class="n">tensor</span><span class="p">,</span> <span class="n">a</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;fan_in&#39;</span><span class="p">,</span> <span class="n">nonlinearity</span><span class="o">=</span><span class="s1">&#39;leaky_relu&#39;</span><span class="p">):</span>
    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Fills the input `Tensor` with values according to the method</span>
<span class="sd">    described in `Delving deep into rectifiers: Surpassing human-level</span>
<span class="sd">    performance on ImageNet classification` - He, K. et al. (2015), using a</span>
<span class="sd">    normal distribution. The resulting tensor will have values sampled from</span>
<span class="sd">    :math:`\mathcal{N}(0, \text{std}^2)` where</span>

<span class="sd">    .. math::</span>
<span class="sd">        \text{std} = \frac{\text{gain}}{\sqrt{\text{fan\_mode}}}</span>

<span class="sd">    Also known as He initialization.</span>

<span class="sd">    Args:</span>
<span class="sd">        tensor: an n-dimensional `torch.Tensor`</span>
<span class="sd">        a: the negative slope of the rectifier used after this layer (only </span>
<span class="sd">        used with ``&#39;leaky_relu&#39;``)</span>
<span class="sd">        mode: either ``&#39;fan_in&#39;`` (default) or ``&#39;fan_out&#39;``. Choosing ``&#39;fan_in&#39;``</span>
<span class="sd">            preserves the magnitude of the variance of the weights in the</span>
<span class="sd">            forward pass. Choosing ``&#39;fan_out&#39;`` preserves the magnitudes in the</span>
<span class="sd">            backwards pass.</span>
<span class="sd">        nonlinearity: the non-linear function (`nn.functional` name),</span>
<span class="sd">            recommended to use only with ``&#39;relu&#39;`` or ``&#39;leaky_relu&#39;`` (default).</span>

<span class="sd">    Examples:</span>
<span class="sd">        &gt;&gt;&gt; w = torch.empty(3, 5)</span>
<span class="sd">        &gt;&gt;&gt; nn.init.kaiming_normal_(w, mode=&#39;fan_out&#39;, nonlinearity=&#39;relu&#39;)</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="n">fan</span> <span class="o">=</span> <span class="n">_calculate_correct_fan</span><span class="p">(</span><span class="n">tensor</span><span class="p">,</span> <span class="n">mode</span><span class="p">)</span>
    <span class="n">gain</span> <span class="o">=</span> <span class="n">calculate_gain</span><span class="p">(</span><span class="n">nonlinearity</span><span class="p">,</span> <span class="n">a</span><span class="p">)</span>
    <span class="n">std</span> <span class="o">=</span> <span class="n">gain</span> <span class="o">/</span> <span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">fan</span><span class="p">)</span>
    <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
        <span class="k">return</span> <span class="n">tensor</span><span class="o">.</span><span class="n">normal_</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">std</span><span class="p">)</span></div>


<div class="viewcode-block" id="orthogonal_"><a class="viewcode-back" href="../../../nn.init.html#torch.nn.init.orthogonal_">[docs]</a><span class="k">def</span> <span class="nf">orthogonal_</span><span class="p">(</span><span class="n">tensor</span><span class="p">,</span> <span class="n">gain</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Fills the input `Tensor` with a (semi) orthogonal matrix, as</span>
<span class="sd">    described in `Exact solutions to the nonlinear dynamics of learning in deep</span>
<span class="sd">    linear neural networks` - Saxe, A. et al. (2013). The input tensor must have</span>
<span class="sd">    at least 2 dimensions, and for tensors with more than 2 dimensions the</span>
<span class="sd">    trailing dimensions are flattened.</span>

<span class="sd">    Args:</span>
<span class="sd">        tensor: an n-dimensional `torch.Tensor`, where :math:`n \geq 2`</span>
<span class="sd">        gain: optional scaling factor</span>

<span class="sd">    Examples:</span>
<span class="sd">        &gt;&gt;&gt; w = torch.empty(3, 5)</span>
<span class="sd">        &gt;&gt;&gt; nn.init.orthogonal_(w)</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">if</span> <span class="n">tensor</span><span class="o">.</span><span class="n">ndimension</span><span class="p">()</span> <span class="o">&lt;</span> <span class="mi">2</span><span class="p">:</span>
        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Only tensors with 2 or more dimensions are supported&quot;</span><span class="p">)</span>

    <span class="n">rows</span> <span class="o">=</span> <span class="n">tensor</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
    <span class="n">cols</span> <span class="o">=</span> <span class="n">tensor</span><span class="o">.</span><span class="n">numel</span><span class="p">()</span> <span class="o">//</span> <span class="n">rows</span>
    <span class="n">flattened</span> <span class="o">=</span> <span class="n">tensor</span><span class="o">.</span><span class="n">new</span><span class="p">(</span><span class="n">rows</span><span class="p">,</span> <span class="n">cols</span><span class="p">)</span><span class="o">.</span><span class="n">normal_</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>

    <span class="k">if</span> <span class="n">rows</span> <span class="o">&lt;</span> <span class="n">cols</span><span class="p">:</span>
        <span class="n">flattened</span><span class="o">.</span><span class="n">t_</span><span class="p">()</span>

    <span class="c1"># Compute the qr factorization</span>
    <span class="n">q</span><span class="p">,</span> <span class="n">r</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">qr</span><span class="p">(</span><span class="n">flattened</span><span class="p">)</span>
    <span class="c1"># Make Q uniform according to https://arxiv.org/pdf/math-ph/0609050.pdf</span>
    <span class="n">d</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">diag</span><span class="p">(</span><span class="n">r</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
    <span class="n">ph</span> <span class="o">=</span> <span class="n">d</span><span class="o">.</span><span class="n">sign</span><span class="p">()</span>
    <span class="n">q</span> <span class="o">*=</span> <span class="n">ph</span>

    <span class="k">if</span> <span class="n">rows</span> <span class="o">&lt;</span> <span class="n">cols</span><span class="p">:</span>
        <span class="n">q</span><span class="o">.</span><span class="n">t_</span><span class="p">()</span>

    <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
        <span class="n">tensor</span><span class="o">.</span><span class="n">view_as</span><span class="p">(</span><span class="n">q</span><span class="p">)</span><span class="o">.</span><span class="n">copy_</span><span class="p">(</span><span class="n">q</span><span class="p">)</span>
        <span class="n">tensor</span><span class="o">.</span><span class="n">mul_</span><span class="p">(</span><span class="n">gain</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">tensor</span></div>


<div class="viewcode-block" id="sparse_"><a class="viewcode-back" href="../../../nn.init.html#torch.nn.init.sparse_">[docs]</a><span class="k">def</span> <span class="nf">sparse_</span><span class="p">(</span><span class="n">tensor</span><span class="p">,</span> <span class="n">sparsity</span><span class="p">,</span> <span class="n">std</span><span class="o">=</span><span class="mf">0.01</span><span class="p">):</span>
    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Fills the 2D input `Tensor` as a sparse matrix, where the</span>
<span class="sd">    non-zero elements will be drawn from the normal distribution</span>
<span class="sd">    :math:`\mathcal{N}(0, 0.01)`, as described in `Deep learning via</span>
<span class="sd">    Hessian-free optimization` - Martens, J. (2010).</span>

<span class="sd">    Args:</span>
<span class="sd">        tensor: an n-dimensional `torch.Tensor`</span>
<span class="sd">        sparsity: The fraction of elements in each column to be set to zero</span>
<span class="sd">        std: the standard deviation of the normal distribution used to generate</span>
<span class="sd">            the non-zero values</span>

<span class="sd">    Examples:</span>
<span class="sd">        &gt;&gt;&gt; w = torch.empty(3, 5)</span>
<span class="sd">        &gt;&gt;&gt; nn.init.sparse_(w, sparsity=0.1)</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">if</span> <span class="n">tensor</span><span class="o">.</span><span class="n">ndimension</span><span class="p">()</span> <span class="o">!=</span> <span class="mi">2</span><span class="p">:</span>
        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Only tensors with 2 dimensions are supported&quot;</span><span class="p">)</span>

    <span class="n">rows</span><span class="p">,</span> <span class="n">cols</span> <span class="o">=</span> <span class="n">tensor</span><span class="o">.</span><span class="n">shape</span>
    <span class="n">num_zeros</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">math</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">sparsity</span> <span class="o">*</span> <span class="n">rows</span><span class="p">))</span>

    <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
        <span class="n">tensor</span><span class="o">.</span><span class="n">normal_</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">std</span><span class="p">)</span>
        <span class="k">for</span> <span class="n">col_idx</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">cols</span><span class="p">):</span>
            <span class="n">row_indices</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randperm</span><span class="p">(</span><span class="n">rows</span><span class="p">)</span>
            <span class="n">zero_indices</span> <span class="o">=</span> <span class="n">row_indices</span><span class="p">[:</span><span class="n">num_zeros</span><span class="p">]</span>
            <span class="n">tensor</span><span class="p">[</span><span class="n">zero_indices</span><span class="p">,</span> <span class="n">col_idx</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
    <span class="k">return</span> <span class="n">tensor</span></div>


<span class="c1"># for backward compatibility</span>
<span class="k">def</span> <span class="nf">_make_deprecate</span><span class="p">(</span><span class="n">meth</span><span class="p">):</span>
    <span class="n">new_name</span> <span class="o">=</span> <span class="n">meth</span><span class="o">.</span><span class="vm">__name__</span>
    <span class="n">old_name</span> <span class="o">=</span> <span class="n">new_name</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>

    <span class="k">def</span> <span class="nf">deprecated_init</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
        <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;nn.init.</span><span class="si">{}</span><span class="s2"> is now deprecated in favor of nn.init.</span><span class="si">{}</span><span class="s2">.&quot;</span>
                      <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">old_name</span><span class="p">,</span> <span class="n">new_name</span><span class="p">),</span> <span class="n">stacklevel</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
        <span class="k">return</span> <span class="n">meth</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>

    <span class="n">deprecated_init</span><span class="o">.</span><span class="vm">__doc__</span> <span class="o">=</span> <span class="sa">r</span><span class="s2">&quot;&quot;&quot;</span>
<span class="s2">    </span><span class="si">{old_name}</span><span class="s2">(...)</span>

<span class="s2">    .. warning::</span>
<span class="s2">        This method is now deprecated in favor of :func:`torch.nn.init.</span><span class="si">{new_name}</span><span class="s2">`.</span>

<span class="s2">    See :func:`~torch.nn.init.</span><span class="si">{new_name}</span><span class="s2">` for details.&quot;&quot;&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
        <span class="n">old_name</span><span class="o">=</span><span class="n">old_name</span><span class="p">,</span> <span class="n">new_name</span><span class="o">=</span><span class="n">new_name</span><span class="p">)</span>
    <span class="n">deprecated_init</span><span class="o">.</span><span class="vm">__name__</span> <span class="o">=</span> <span class="n">old_name</span>
    <span class="k">return</span> <span class="n">deprecated_init</span>


<span class="n">uniform</span> <span class="o">=</span> <span class="n">_make_deprecate</span><span class="p">(</span><span class="n">uniform_</span><span class="p">)</span>
<span class="n">normal</span> <span class="o">=</span> <span class="n">_make_deprecate</span><span class="p">(</span><span class="n">normal_</span><span class="p">)</span>
<span class="n">constant</span> <span class="o">=</span> <span class="n">_make_deprecate</span><span class="p">(</span><span class="n">constant_</span><span class="p">)</span>
<span class="n">eye</span> <span class="o">=</span> <span class="n">_make_deprecate</span><span class="p">(</span><span class="n">eye_</span><span class="p">)</span>
<span class="n">dirac</span> <span class="o">=</span> <span class="n">_make_deprecate</span><span class="p">(</span><span class="n">dirac_</span><span class="p">)</span>
<span class="n">xavier_uniform</span> <span class="o">=</span> <span class="n">_make_deprecate</span><span class="p">(</span><span class="n">xavier_uniform_</span><span class="p">)</span>
<span class="n">xavier_normal</span> <span class="o">=</span> <span class="n">_make_deprecate</span><span class="p">(</span><span class="n">xavier_normal_</span><span class="p">)</span>
<span class="n">kaiming_uniform</span> <span class="o">=</span> <span class="n">_make_deprecate</span><span class="p">(</span><span class="n">kaiming_uniform_</span><span class="p">)</span>
<span class="n">kaiming_normal</span> <span class="o">=</span> <span class="n">_make_deprecate</span><span class="p">(</span><span class="n">kaiming_normal_</span><span class="p">)</span>
<span class="n">orthogonal</span> <span class="o">=</span> <span class="n">_make_deprecate</span><span class="p">(</span><span class="n">orthogonal_</span><span class="p">)</span>
<span class="n">sparse</span> <span class="o">=</span> <span class="n">_make_deprecate</span><span class="p">(</span><span class="n">sparse_</span><span class="p">)</span>
</pre></div>

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